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Abstract. Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

Executive SummaryWashington River Protection Solutions (WRPS), as the Hanford Site tank farms contractor, will be responsible for transferring waste from a double-shell tank (DST) to the Waste Treatment and Immobilization Plant (WTP) when the WTP begins operations. The WRPS approach for predicting mixing and transfer performance (MTP) for a DST involves three primary steps:1. Conduct tests consisting of variations in several parameters in two scaled tanks in the Small Scale Mixing Demonstration (SSMD) platform to generate data on MTP.2. Develop scaling relationships for MTP as a function of the test parameters using the test data from Step 1. Use the scaling relationships fromStep 2 that are valid for the two test scales, as well as other knowledge and methods for mixing and transfer, to develop scaling relationships applicable to full-scale DST performance.Step 1 has been completed by WRPS, which involved generating data for 26 test combinations performed in both of the two scaled tanks of the SSMD platform (for a total of 52 tests). The two tanks had diameters of 43.2 (referred to as 43 subsequently) and 120 inches, which are 1:21 and 1:8 relative to a full-scale DST, respectively. Other parameters that were varied in testing included mixer-jet nozzle velocity (U), base simulant (BS), supernatant viscosity (SV), (a) and transfer-line capture velocity (CV). The first 22 of the 26 test combinations conducted in both size tanks were selected using a statistical experimental design approach. An additional 4 test combinations performed at both scales used parameter combinations that are most relevant to expected operating conditions, and had not been tested previously. For each of the 52 tests, samples were collected pre-transfer and for each of five batch transfers. The samples were prepared and analyzed, with the results being the concentrations (lb/gal slurry) of the four solids components (gibbsite, stainless steel, sand, and ZrO 2 ) in the base simulant.Step 2 consists of building mathematical models for each of the two scale tanks that describe the MTP as a function of the test parameters. These models can then be used to calculate mixer-jet nozzle velocities where performance is equal between tank scales. These points of equal performance can be used to derive scaling relationships, which will allow estimating performance for full-scale DSTs. This report documents the statistical analyses associated with Step 2, which were performed by Pacific Northwest National Laboratory (PNNL) on data from the 52 tests conducted. Preliminary efforts focused on trying to model MTP metrics consisting of differences or ratios of BS-component concentrations for the five batch transfers relative to pre-transfer. However, depending on the solids component, there was little difference in component concentrations in batch transfers compared to pre-transfer for many to most of the tests. Hence, such differences and ratios of component concentrations were not useful MTP metrics because the effects of the test parameters "canceled ...

SummaryDuring fiscal year 2012, a team from the Pacific Northwest National Laboratory conducted an assessment and analysis of the Second Line of Defense (SLD) Sustainability spare parts program. Spare parts management touches many aspects of the SLD Sustainability program including contracting and integration of local maintenance providers, equipment vendors, analyses and metrics on program performance, system state of health, and maintenance practices. Standardized spares management will provide better data for decisions during the site transition phase and facilitate transition to partner country sustainability ownership. The effort was coordinated with related Sustainability program initiatives, including a configuration items baselining initiative, a metrics initiative, and a maintenance initiative.The spares study has also led to pilot programs for sourcing alternatives that include regional intermediate inventories and partnering agreements that leverage existing supply chains. Many partners from the Sustainability program contributed to and were consulted in the course of the study. This document provides a description of the findings, recommendations, and implemented solutions that have resulted from the study. The recommendations are organized in broad categories.Spares data management. Concerned with how information about spares is gathered, maintained, and made available to support decisions and business processes throughout the program. This work included development of a comprehensive SLD Spare Parts Database and Catalog built from various information sources.Integration of spares data with site configuration management. A way forward using unambiguous spare parts designations and authoritative information about site configurations to make better programlevel decisions about spares inventory levels, sourcing, shortfalls, and upgrade, refresh, or retrofit.Spares inventory management. Clear designation of parts for better program-wide visibility and analysis of inventories. Spares supporting processes.Better spares data to permit functions such as demand-supported inventory management, failure or risk-aware cost projections, and statistical failure analysis.At closeout of the spares study, the spares database is being transitioned to the control of the Help Desk Portal for database configuration management and integration with Portal functions. The derived spares catalog views are being shared with the SLD Sustainability community to validate design decisions; cleanup erroneous, uncertain, duplicate, or missing data elements; and elicit use cases and requirements that can be implemented in future work or enable ongoing and future initiatives. vii Acronyms and Abbreviations